Rahul Savani

Reinforcement Learning · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 64 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 75 citations into it; #1,022 of 2,242 by trust here, #1,344 by citations
• Artificial Intelligence — 56 citations into it; #12,858 of 35,345 by trust here, #15,236 by citations
• Management Science and Operations Research — 20 citations into it; #6,814 of 11,946 by trust here, #9,931 by citations
• Game & Decision Theory — 16 citations into it; #196 of 249 by trust here, #149 by citations
• Computational Theory and Mathematics — 15 citations into it
• Control and Systems Engineering — 10 citations into it
• Electrical and Electronic Engineering — 9 citations into it; #10,606 of 16,539 by trust here, #9,631 by citations
• Computer Networks and Communications — 6 citations into it

Whom Rahul Savani trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. Diederik P. Kingma
3. Ermo Wei
4. Laëtitia Matignon
5. Volodymyr Mnih
6. Hado van Hasselt
7. Jakob N. Foerster
8. Geoffrey E. Hinton
9. Jimmy Ba
10. Sean Luke

Who trusts Rahul Savani overall

1. Pablo Hernandez-Leal
2. Sven Gronauer
3. Xueguang Lyu
4. Bilal Kartal
5. Klaus-Jürgen Diepold

Papers from Rahul Savani's perspective

Ordered by how strongly the researchers Rahul Savani trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Reinforcement Learning: An Introduction
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Human-level control through deep reinforcement learning
8. Attention is All you Need (Neural Information Processing Systems)
9. Asynchronous Methods for Deep Reinforcement Learning
10. ImageNet: A large-scale hierarchical image database

Their papers

• Lenient Multi-Agent Deep Reinforcement Learning (2017) — 168 citations
• Negative Update Intervals in Deep Multi-Agent Reinforcement Learning (2018) — 28 citations
• The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning (2019) — 45 citations